Dieter Kraus

On least squares methods for direction of arrival estimation in the presence of unknown noise fields

The direction-of-arrival estimation of signal wavefronts in the presence of unknown noise fields is investigated. Generalizations of known criteria for both conditional and nonconditional maximum-likelihood estimates are developed. Numerical calculations show that the usual Gauss-Newton iteration for conditional maximum-likelihood estimates cannot give good results. Therefore, a related, relatively simple two-step least-squares estimate is constructed. Results of numerical experiments are presented and indicate that the two-step estimate has approximately the same power as the least-squares estimate using the exact noise correlation structure.<<ETX>>

Parametric Methods for Source Location Estimation

Abstract Approximate maximum-likelihood estimates for locating of wide band sources in the presence of partly unknown noise fields are developed. Alternatively, two least squares methods fitting a parametric model of the spectral density matrix to a corresponding nonparametric estimate are investigated. Furthermore, applying the expectation maximization (EM) algorithm, a computationally robust iteration scheme for maximizing the log-likelihood function is derived. Using wide band data from a North Sea experiment, we compare the performance of the maximum likelihood method via the EM algorithm with the MUSIC algorithm combined with the rotational signal-subspace (RSS) focussing technique.

Asymptotic and empirical results on approximate maximum likelihood and least squares estimates for sensor array processing

The problem of source location estimation in the presence of partly unknown noise fields is addressed. A novel two-stage procedure is developed which combines the conditional maximum-likelihood estimate and the conditional marginal maximum-likelihood estimate for the signal parameters and the noise parameters, respectively. The strong consistency and asymptotic normality of the conditional maximum-likelihood estimates for location parameters and noise parameters in the case of not necessarily normal and independent distributed observations are proved. Alternatively, different least squares criteria fitting a parametric model of the spectral density matrix to a nonparametric consistent estimate of the spectral density matrix are investigated and their asymptotic behaviors are mentioned briefly. Results of numerical experiments are presented to show the performance of the different estimates.<<ETX>>

On moments of complex Wishart and complex inverse Wishart distributed matrices

This paper addresses the calculation of moments of complex Wishart and complex inverse Wishart distributed random matrices. Complex Wishart and complex inverse Wishart distributed random matrices are used in applications like radar, sonar, or seismology in order to model the statistical properties of complex sample covariance matrices and complex inverse sample covariance matrices, respectively. The moments of these random matrices are often needed e.g. in studies of asymptotic properties of parameter estimates. This paper gives a derivation of the probability density function of complex inverse Wishart distributed random matrices. Furthermore, strategies are outlined for the calculation of the moments of complex Wishart and complex inverse Wishart distributed matrices.

Contributions to Automatic Target Recognition Systems for Underwater Mine Classification

This paper deals with several original contributions to an automatic target recognition (ATR) system, which is applied to underwater mine classification. The contributions concentrate on feature selection and object classification. First, a sophisticated filter method is designed for the feature selection. This filter method utilizes a novel feature relevance measure, the composite relevance measure (CRM). Feature relevance measures in the literature (e.g., mutual information and relief weight) evaluate the features only with respect to certain aspects. The CRM is a combination of several measures so that it is able to provide a more comprehensive assessment of the features. Both linear and nonlinear combinations of these measures are taken into account. A wide range of classifiers is able to provide satisfactory classification results by using the features selected according to the CRM. Second, in the step of object classification, an ensemble learning scheme in the framework of the Dempster-Shafer theory is introduced to fuse the results obtained by different classifiers. This fusion can improve the classification performance. We propose a reasonable construction of the basic belief assignment (BBA). The BBA considers both the reliability of the classifiers and the support of individual classifiers provided to the hypotheses about the types of test objects. Finally, this ATR system is applied to real synthetic aperture sonar imagery to evaluate its performance.

Unsupervised image segmentation and image fusion for multi-beam/multi-aspect sidescan sonar images

In the development of future sonar systems, computer aided classification (CAC) becomes increasingly important. One element in a Multi-Beam/Multi-Aspect sidescan sonar CAC system is the segmentation and image fusion of the multidimensional data. Recent developments have been made to prevent the effects of aspect dependent backscattering strength (target strength) of objects lying on the seafloor. Because the target strength of an object varies with aspect angle, the sonar echo and consequently its representation in the sidescan image is rather random. To make sure maximum echo strength is obtained, overlapping bottom areas are insonified by temporal successive pings giving the echoes of the target under different aspect angles. The resulting images of successive pings are fused to one sidescan image. The existing image fusion algorithms now require an operator to set threshold values distinguishing target and shadow zones from bottom reverberation zones. The authors propose an unsupervised method for segmentation and optimal fusion of multidimensional sidescan sonar images for Multi-Beam/Multi-Aspect sidescan sonars.

Maximum Likelihood Location Estimation of Wideband Sources Using the EM Algorithm

Abstract Approximate maximum likelihood and related estimation techniques for source location estimation are investigated. An extended model of the spectral density matrix of the sensor array output for coherent sources (multipath propagation) is introduced. We show that the EM algorithm can be successfully appplied for this special case of practical importance. In contrast to [2], the EM iteration scheme is derived by exploiting that the distribution type of the finite Fourier- transformed sensor outputs belongs to the exponential family. This approach requires neither the additional noise parameters used in [2] nor an alternating step by step optimization of spectral and source location parameters. Finally, we investigate the co-called approximate dual maximum likelihood estimate.

Extended active contours approach for mine detection in synthetic aperture sonar images

In this paper the problem of contour extraction in sonar images is addressed. We talk in this context about naval mines placed on the seafloor which are still a vast restraint in civil and military shipping. This potential risk is typically encountered by advanced sonar signal processing techniques and a huge amount of human interactions. To reduce at least the human interactions an automatic procedure is desired. Therefore we introduce a novel automatic target extraction algorithm based on active contours employing a specific shadow locating energy motivated by our experiments. Additionally we use a K-means based thresholding process and a Kolmogorov Smirnov (KS) test for improving the initial guess and therefore optimizing the overall performance.

3D-Sonar image formation and shape recognition techniques

In future developments of AUV systems 3D image processing techniques will gain in importance. The high resolution 3D-Sonar can be considered as the “eye” of the AUV which provides acoustical information utilized for the detection and classification of anomalies, e.g. at ships hulls. Therefore, the investigations presented include the simulation of underwater scenes, object shapes and AUV movements as well as the modelling of the acoustical system performance. The obtained beam signals provide the input to the 3D image formation and shape recognition algorithms. Beside conventional 3D interpolation and voxel image formation techniques we propose a novel image formation approach by exploiting the maximum of each beam signal. Moreover, efficient noise suppression algorithms are developed and validated by employing multi-beam/multi-aspect imaging. Finally, the proposed 3D image formation technique is computationally efficient, robust against noise and allows the application of conventional 2D image processing algorithms.

EM dual maximum likelihood estimation for wideband source location

Approximate maximum likelihood estimates and approximate dual maximum likelihood estimates for locating wideband sources in the presence of partly unknown noise fields are investigated. An extended model of the spectral density matrix of the sensor array output for coherent sources (multipath propagation) is introduced. The authors derive an expectation maximization (EM) iteration scheme for distributions belonging to the exponential family and investigate the so-called dual maximum likelihood estimate. The EM procedure is used to compute approximate maximum likelihood and approximate dual maximum likelihood estimates for source locations, signal, and noise spectral parameters.<<ETX>>